Tobacco Stem Object Detection Based on Improved YOLO

The threshing and redrying process is a crucial stage in cigarette production. During this stage, threshing equipment is used to separate the stems from the initially dried tobacco leaves, followed by the redrying of the leaves. Tobacco stems in the leaves affect various cigarette quality evaluation indices, making stem detection extremely important. In actual production, the methods used are mostly manual screening or winnowing equipment. Existing computer vision-based methods also require offline sampling and manual spreading for detection. The above methods are time-consuming and labor-intensive. Therefore, we propose a tobacco stem detection method based on an improved YOLO model, capable of directly detecting tobacco stems in the mixed materials on the production line without the need for offline sampling. This method, built on the YOLO framework, incorporates the SimAM attention module to make the network focus more on the tobacco stem regions. Additionally, it uses a texture enhancement module to extract concealed edge features of the stems, ultimately achieving a detection accuracy (AP<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</inf>) of 83.6%. This method can effectively improve the detection efficiency of tobacco stems in the threshing and redrying process.